Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fa894d38358>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fa894c72710>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = "real_input")
    z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, name = 'learning_rate')
    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [16]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    with tf.variable_scope("discriminator",reuse=reuse):
        alpha=0.2

        l1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        l1 = tf.maximum(l1 * alpha, l1)

        l2 = tf.layers.conv2d(l1, 128, 5, strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2,training=True)
        l2 = tf.maximum(l2 * alpha, l2)
        
        l3 = tf.layers.conv2d(l2, 256, 5, strides=1, padding='same')
        l3 = tf.layers.batch_normalization(l2,training=True)
        l3 = tf.maximum(l3 * alpha, l2)
        
        flat = tf.reshape(l2, (-1,7*7*256))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [17]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """

    reuse = not is_train
    
    with tf.variable_scope("generator", reuse=reuse):
        alpha = 0.2
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=1, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 16x16x128 now

        # 28x28x(out_channel_dimension) now
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        return tf.tanh(logits)



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    # get counterfit from generator, 
    # get output from discriminator using real input
    # get output from discriminator using counterfit generated
    g_out = generator(input_z,out_channel_dim=out_channel_dim)
    d_real_out, d_real_logit = discriminator(input_real)
    d_fake_out, d_fake_logit = discriminator(g_out,reuse=True)
    
    #real label smoothing
    smooth = 0.1
    
    # shortcut loss calculation function
    calculate_loss = lambda logits,labels : tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,labels=labels))
    
    d_loss_real = calculate_loss(d_real_logit, tf.ones_like(d_real_out) * (1 - smooth))
    d_loss_fake = calculate_loss(d_fake_logit, tf.zeros_like(d_fake_out))
    
    g_loss = calculate_loss(d_fake_logit, tf.ones_like(d_fake_out))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    
    # get variables for `var_list` minimize parameter
    trainables = tf.trainable_variables()

    # filter those by generator/discriminator
    gen_train = [operation for operation in trainables if operation.name.startswith('generator')]
    dis_train = [operation for operation in trainables if operation.name.startswith('discriminator')]

    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    # use with control dependencies for batch normalization calculations updating
    with tf.control_dependencies(g_update_ops):
        # calculate optimizations
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list=gen_train)

    # use with control dependencies for batch normalization calculations updating
    with tf.control_dependencies(d_update_ops):
        # calculate optimizations
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list=dis_train)
        
    return g_train_opt, d_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    real_input, z_input, lr = model_inputs(data_shape[1],
                                           data_shape[2], 
                                           data_shape[3], 
                                           z_dim)
    
    d_loss, g_loss = model_loss(real_input, 
                                z_input, 
                                data_shape[3])
    
    d_opt, g_opt = model_opt(d_loss,
                             g_loss,
                             learning_rate,
                             beta1)
    
    steps, step_text_update, step_example_update = 0, 10, 100
    sample_size = 20
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps+=1
                
                # resize b/c we used -.5 to .5 instead of -1 to 1
                batch_images = batch_images * 2
                
                # random sample noise 
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                #run optimizers
                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: batch_z})
                _ = sess.run(g_opt, feed_dict={z_input: batch_z, lr: learning_rate, real_input: batch_images})
                
                if steps % step_text_update == 0:
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("epoch {}/{} ***".format(epoch_i+1, epochs),
                          "discriminator loss: {:.4f} ***".format(train_loss_d),
                          "generator loss: {:.4f}".format(train_loss_g))                    
                
                if steps % step_example_update == 0:
                    show_generator_output(sess, sample_size, z_input, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [21]:
batch_size = 16
z_dim = 100
learning_rate = 0.00035
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
epoch 1/2 *** discriminator loss: 0.5838 *** generator loss: 3.1138
epoch 1/2 *** discriminator loss: 0.4437 *** generator loss: 3.4133
epoch 1/2 *** discriminator loss: 0.7852 *** generator loss: 3.0259
epoch 1/2 *** discriminator loss: 1.1554 *** generator loss: 2.0774
epoch 1/2 *** discriminator loss: 0.8764 *** generator loss: 2.1765
epoch 1/2 *** discriminator loss: 0.9143 *** generator loss: 1.6768
epoch 1/2 *** discriminator loss: 0.8995 *** generator loss: 2.3173
epoch 1/2 *** discriminator loss: 1.4612 *** generator loss: 1.8836
epoch 1/2 *** discriminator loss: 1.2433 *** generator loss: 0.8527
epoch 1/2 *** discriminator loss: 1.3852 *** generator loss: 1.4951
epoch 1/2 *** discriminator loss: 1.1153 *** generator loss: 1.0807
epoch 1/2 *** discriminator loss: 1.0258 *** generator loss: 1.3943
epoch 1/2 *** discriminator loss: 1.3662 *** generator loss: 0.8937
epoch 1/2 *** discriminator loss: 1.6661 *** generator loss: 0.9351
epoch 1/2 *** discriminator loss: 1.1559 *** generator loss: 1.1223
epoch 1/2 *** discriminator loss: 1.0740 *** generator loss: 1.2395
epoch 1/2 *** discriminator loss: 1.0157 *** generator loss: 1.2281
epoch 1/2 *** discriminator loss: 1.2558 *** generator loss: 1.1629
epoch 1/2 *** discriminator loss: 1.2070 *** generator loss: 1.3110
epoch 1/2 *** discriminator loss: 1.2275 *** generator loss: 0.8099
epoch 1/2 *** discriminator loss: 1.1111 *** generator loss: 1.3757
epoch 1/2 *** discriminator loss: 1.0976 *** generator loss: 0.8942
epoch 1/2 *** discriminator loss: 1.1851 *** generator loss: 1.3254
epoch 1/2 *** discriminator loss: 1.1483 *** generator loss: 0.9251
epoch 1/2 *** discriminator loss: 1.2391 *** generator loss: 1.0293
epoch 1/2 *** discriminator loss: 1.2648 *** generator loss: 0.8283
epoch 1/2 *** discriminator loss: 1.0786 *** generator loss: 1.4334
epoch 1/2 *** discriminator loss: 1.1687 *** generator loss: 1.3894
epoch 1/2 *** discriminator loss: 1.2026 *** generator loss: 1.2870
epoch 1/2 *** discriminator loss: 1.3071 *** generator loss: 1.4303
epoch 1/2 *** discriminator loss: 0.9853 *** generator loss: 1.6745
epoch 1/2 *** discriminator loss: 1.4211 *** generator loss: 1.0476
epoch 1/2 *** discriminator loss: 1.1555 *** generator loss: 1.3099
epoch 1/2 *** discriminator loss: 1.0623 *** generator loss: 1.5978
epoch 1/2 *** discriminator loss: 1.1831 *** generator loss: 1.1431
epoch 1/2 *** discriminator loss: 1.1282 *** generator loss: 0.9340
epoch 1/2 *** discriminator loss: 1.1234 *** generator loss: 1.4517
epoch 1/2 *** discriminator loss: 1.2910 *** generator loss: 0.7816
epoch 1/2 *** discriminator loss: 1.2104 *** generator loss: 0.8026
epoch 1/2 *** discriminator loss: 1.4649 *** generator loss: 0.4853
epoch 1/2 *** discriminator loss: 1.2578 *** generator loss: 1.1377
epoch 1/2 *** discriminator loss: 1.1434 *** generator loss: 0.8065
epoch 1/2 *** discriminator loss: 1.0679 *** generator loss: 1.4996
epoch 1/2 *** discriminator loss: 1.3276 *** generator loss: 1.4625
epoch 1/2 *** discriminator loss: 0.9997 *** generator loss: 1.3934
epoch 1/2 *** discriminator loss: 1.1225 *** generator loss: 1.1432
epoch 1/2 *** discriminator loss: 1.1889 *** generator loss: 1.4543
epoch 1/2 *** discriminator loss: 1.1848 *** generator loss: 0.9571
epoch 1/2 *** discriminator loss: 1.2753 *** generator loss: 1.1394
epoch 1/2 *** discriminator loss: 1.1424 *** generator loss: 1.0756
epoch 1/2 *** discriminator loss: 1.4102 *** generator loss: 0.5822
epoch 1/2 *** discriminator loss: 1.1535 *** generator loss: 0.9220
epoch 1/2 *** discriminator loss: 1.1603 *** generator loss: 1.2039
epoch 1/2 *** discriminator loss: 1.2803 *** generator loss: 0.6265
epoch 1/2 *** discriminator loss: 1.3707 *** generator loss: 0.6503
epoch 1/2 *** discriminator loss: 1.2780 *** generator loss: 1.4649
epoch 1/2 *** discriminator loss: 1.3341 *** generator loss: 0.9737
epoch 1/2 *** discriminator loss: 1.1660 *** generator loss: 0.9886
epoch 1/2 *** discriminator loss: 0.9916 *** generator loss: 1.2293
epoch 1/2 *** discriminator loss: 1.1048 *** generator loss: 1.2246
epoch 1/2 *** discriminator loss: 1.3082 *** generator loss: 1.2629
epoch 1/2 *** discriminator loss: 1.2297 *** generator loss: 1.3401
epoch 1/2 *** discriminator loss: 1.3795 *** generator loss: 1.7622
epoch 1/2 *** discriminator loss: 1.1452 *** generator loss: 1.4058
epoch 1/2 *** discriminator loss: 1.2661 *** generator loss: 0.7382
epoch 1/2 *** discriminator loss: 1.0618 *** generator loss: 1.0852
epoch 1/2 *** discriminator loss: 1.2378 *** generator loss: 0.8106
epoch 1/2 *** discriminator loss: 1.0080 *** generator loss: 1.3931
epoch 1/2 *** discriminator loss: 1.3933 *** generator loss: 0.6122
epoch 1/2 *** discriminator loss: 1.0786 *** generator loss: 1.2023
epoch 1/2 *** discriminator loss: 1.2063 *** generator loss: 0.7964
epoch 1/2 *** discriminator loss: 1.1742 *** generator loss: 1.0357
epoch 1/2 *** discriminator loss: 1.2907 *** generator loss: 0.9972
epoch 1/2 *** discriminator loss: 1.1262 *** generator loss: 0.8237
epoch 1/2 *** discriminator loss: 1.2227 *** generator loss: 0.8630
epoch 1/2 *** discriminator loss: 1.3692 *** generator loss: 0.6043
epoch 1/2 *** discriminator loss: 1.2470 *** generator loss: 0.8733
epoch 1/2 *** discriminator loss: 1.1915 *** generator loss: 1.1908
epoch 1/2 *** discriminator loss: 1.3324 *** generator loss: 0.7198
epoch 1/2 *** discriminator loss: 1.2571 *** generator loss: 0.7695
epoch 1/2 *** discriminator loss: 1.3174 *** generator loss: 1.1759
epoch 1/2 *** discriminator loss: 1.1140 *** generator loss: 1.0063
epoch 1/2 *** discriminator loss: 1.2447 *** generator loss: 0.9833
epoch 1/2 *** discriminator loss: 1.3916 *** generator loss: 0.8876
epoch 1/2 *** discriminator loss: 1.3141 *** generator loss: 1.3573
epoch 1/2 *** discriminator loss: 1.2079 *** generator loss: 1.0187
epoch 1/2 *** discriminator loss: 1.0941 *** generator loss: 1.3399
epoch 1/2 *** discriminator loss: 1.0763 *** generator loss: 0.9450
epoch 1/2 *** discriminator loss: 1.3104 *** generator loss: 0.9083
epoch 1/2 *** discriminator loss: 1.3571 *** generator loss: 1.0193
epoch 1/2 *** discriminator loss: 1.4948 *** generator loss: 0.5524
epoch 1/2 *** discriminator loss: 1.3499 *** generator loss: 0.7530
epoch 1/2 *** discriminator loss: 1.3781 *** generator loss: 0.7379
epoch 1/2 *** discriminator loss: 1.2949 *** generator loss: 1.3923
epoch 1/2 *** discriminator loss: 1.2251 *** generator loss: 1.3339
epoch 1/2 *** discriminator loss: 1.3778 *** generator loss: 0.7589
epoch 1/2 *** discriminator loss: 1.2733 *** generator loss: 1.6122
epoch 1/2 *** discriminator loss: 1.2722 *** generator loss: 0.8283
epoch 1/2 *** discriminator loss: 1.2455 *** generator loss: 0.9742
epoch 1/2 *** discriminator loss: 1.1869 *** generator loss: 0.8041
epoch 1/2 *** discriminator loss: 1.1588 *** generator loss: 1.2257
epoch 1/2 *** discriminator loss: 1.2154 *** generator loss: 0.9279
epoch 1/2 *** discriminator loss: 1.1983 *** generator loss: 0.8712
epoch 1/2 *** discriminator loss: 1.3007 *** generator loss: 1.0056
epoch 1/2 *** discriminator loss: 1.3177 *** generator loss: 0.8998
epoch 1/2 *** discriminator loss: 1.2758 *** generator loss: 0.9987
epoch 1/2 *** discriminator loss: 1.2430 *** generator loss: 0.8404
epoch 1/2 *** discriminator loss: 1.0809 *** generator loss: 1.2571
epoch 1/2 *** discriminator loss: 1.2208 *** generator loss: 0.7069
epoch 1/2 *** discriminator loss: 1.0799 *** generator loss: 1.0466
epoch 1/2 *** discriminator loss: 1.1497 *** generator loss: 1.2256
epoch 1/2 *** discriminator loss: 1.3728 *** generator loss: 0.9567
epoch 1/2 *** discriminator loss: 1.2753 *** generator loss: 0.7971
epoch 1/2 *** discriminator loss: 1.3868 *** generator loss: 1.1107
epoch 1/2 *** discriminator loss: 1.3221 *** generator loss: 1.2323
epoch 1/2 *** discriminator loss: 1.2885 *** generator loss: 1.0876
epoch 1/2 *** discriminator loss: 1.2779 *** generator loss: 1.1558
epoch 1/2 *** discriminator loss: 1.1641 *** generator loss: 1.2459
epoch 1/2 *** discriminator loss: 1.2071 *** generator loss: 0.8556
epoch 1/2 *** discriminator loss: 1.0705 *** generator loss: 1.2611
epoch 1/2 *** discriminator loss: 1.2362 *** generator loss: 0.7333
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epoch 2/2 *** discriminator loss: 1.2734 *** generator loss: 1.6543
epoch 2/2 *** discriminator loss: 1.0670 *** generator loss: 1.8220
epoch 2/2 *** discriminator loss: 1.1163 *** generator loss: 0.7877
epoch 2/2 *** discriminator loss: 0.8010 *** generator loss: 1.9307
epoch 2/2 *** discriminator loss: 0.9524 *** generator loss: 1.3866
epoch 2/2 *** discriminator loss: 0.9735 *** generator loss: 1.1454
epoch 2/2 *** discriminator loss: 0.9911 *** generator loss: 0.9808
epoch 2/2 *** discriminator loss: 0.9428 *** generator loss: 1.1988
epoch 2/2 *** discriminator loss: 0.9502 *** generator loss: 1.1127
epoch 2/2 *** discriminator loss: 1.0003 *** generator loss: 1.7011
epoch 2/2 *** discriminator loss: 0.8797 *** generator loss: 1.1719
epoch 2/2 *** discriminator loss: 1.0725 *** generator loss: 1.0106
epoch 2/2 *** discriminator loss: 0.8840 *** generator loss: 1.4878
epoch 2/2 *** discriminator loss: 0.9566 *** generator loss: 0.9585
epoch 2/2 *** discriminator loss: 0.8908 *** generator loss: 1.5853
epoch 2/2 *** discriminator loss: 0.9687 *** generator loss: 1.2036
epoch 2/2 *** discriminator loss: 0.7431 *** generator loss: 1.7365
epoch 2/2 *** discriminator loss: 1.1281 *** generator loss: 0.7589
epoch 2/2 *** discriminator loss: 0.9262 *** generator loss: 1.1038
epoch 2/2 *** discriminator loss: 0.9248 *** generator loss: 0.9655
epoch 2/2 *** discriminator loss: 1.1244 *** generator loss: 0.7639
epoch 2/2 *** discriminator loss: 1.0562 *** generator loss: 1.2139
epoch 2/2 *** discriminator loss: 1.0201 *** generator loss: 1.0783
epoch 2/2 *** discriminator loss: 0.8060 *** generator loss: 1.3247
epoch 2/2 *** discriminator loss: 1.0174 *** generator loss: 0.9792
epoch 2/2 *** discriminator loss: 1.1014 *** generator loss: 1.8039
epoch 2/2 *** discriminator loss: 0.8695 *** generator loss: 1.6039
epoch 2/2 *** discriminator loss: 0.8975 *** generator loss: 1.0090
epoch 2/2 *** discriminator loss: 0.9116 *** generator loss: 1.2086
epoch 2/2 *** discriminator loss: 0.9455 *** generator loss: 1.5639
epoch 2/2 *** discriminator loss: 1.0438 *** generator loss: 1.3872
epoch 2/2 *** discriminator loss: 0.9937 *** generator loss: 0.9178
epoch 2/2 *** discriminator loss: 1.6265 *** generator loss: 2.3389
epoch 2/2 *** discriminator loss: 0.9423 *** generator loss: 1.8441
epoch 2/2 *** discriminator loss: 1.0247 *** generator loss: 0.7537
epoch 2/2 *** discriminator loss: 0.8319 *** generator loss: 1.4427
epoch 2/2 *** discriminator loss: 0.7681 *** generator loss: 1.5161
epoch 2/2 *** discriminator loss: 0.8164 *** generator loss: 1.5825
epoch 2/2 *** discriminator loss: 0.9288 *** generator loss: 1.2822
epoch 2/2 *** discriminator loss: 0.9623 *** generator loss: 1.9680
epoch 2/2 *** discriminator loss: 0.8352 *** generator loss: 1.4581
epoch 2/2 *** discriminator loss: 0.8053 *** generator loss: 2.3349
epoch 2/2 *** discriminator loss: 1.0769 *** generator loss: 1.0628
epoch 2/2 *** discriminator loss: 1.1472 *** generator loss: 1.1962
epoch 2/2 *** discriminator loss: 0.7780 *** generator loss: 1.4582
epoch 2/2 *** discriminator loss: 0.9227 *** generator loss: 1.5965
epoch 2/2 *** discriminator loss: 1.0552 *** generator loss: 0.9240
epoch 2/2 *** discriminator loss: 1.0572 *** generator loss: 1.5578
epoch 2/2 *** discriminator loss: 0.9242 *** generator loss: 1.2891
epoch 2/2 *** discriminator loss: 0.9746 *** generator loss: 0.8549
epoch 2/2 *** discriminator loss: 0.9015 *** generator loss: 1.9374
epoch 2/2 *** discriminator loss: 0.9678 *** generator loss: 1.0268
epoch 2/2 *** discriminator loss: 0.9597 *** generator loss: 1.1994
epoch 2/2 *** discriminator loss: 0.8625 *** generator loss: 1.3528
epoch 2/2 *** discriminator loss: 0.8137 *** generator loss: 1.2614
epoch 2/2 *** discriminator loss: 0.9209 *** generator loss: 1.9981
epoch 2/2 *** discriminator loss: 1.4590 *** generator loss: 0.4928
epoch 2/2 *** discriminator loss: 0.8456 *** generator loss: 1.2934
epoch 2/2 *** discriminator loss: 0.9357 *** generator loss: 1.3116
epoch 2/2 *** discriminator loss: 1.0625 *** generator loss: 1.5279
epoch 2/2 *** discriminator loss: 0.9689 *** generator loss: 1.3907
epoch 2/2 *** discriminator loss: 0.9046 *** generator loss: 1.5475
epoch 2/2 *** discriminator loss: 1.0708 *** generator loss: 0.8029
epoch 2/2 *** discriminator loss: 1.1641 *** generator loss: 0.7835
epoch 2/2 *** discriminator loss: 0.9932 *** generator loss: 1.1866
epoch 2/2 *** discriminator loss: 0.9353 *** generator loss: 1.7690
epoch 2/2 *** discriminator loss: 0.9548 *** generator loss: 2.2129
epoch 2/2 *** discriminator loss: 1.0951 *** generator loss: 1.5572
epoch 2/2 *** discriminator loss: 0.9023 *** generator loss: 1.5033
epoch 2/2 *** discriminator loss: 1.1772 *** generator loss: 1.4956
epoch 2/2 *** discriminator loss: 1.0020 *** generator loss: 0.8259
epoch 2/2 *** discriminator loss: 1.1210 *** generator loss: 1.2613
epoch 2/2 *** discriminator loss: 0.9736 *** generator loss: 1.4784
epoch 2/2 *** discriminator loss: 0.8285 *** generator loss: 1.6896
epoch 2/2 *** discriminator loss: 0.8773 *** generator loss: 1.4353
epoch 2/2 *** discriminator loss: 1.5611 *** generator loss: 0.3863
epoch 2/2 *** discriminator loss: 0.9976 *** generator loss: 0.8191
epoch 2/2 *** discriminator loss: 1.0184 *** generator loss: 1.5433
epoch 2/2 *** discriminator loss: 0.9379 *** generator loss: 1.3004
epoch 2/2 *** discriminator loss: 0.8768 *** generator loss: 1.2354
epoch 2/2 *** discriminator loss: 1.0056 *** generator loss: 1.7331
epoch 2/2 *** discriminator loss: 1.1152 *** generator loss: 1.2232
epoch 2/2 *** discriminator loss: 0.9773 *** generator loss: 1.1347
epoch 2/2 *** discriminator loss: 0.8598 *** generator loss: 1.2892
epoch 2/2 *** discriminator loss: 1.0063 *** generator loss: 2.2033
epoch 2/2 *** discriminator loss: 1.0100 *** generator loss: 1.7521
epoch 2/2 *** discriminator loss: 1.0676 *** generator loss: 0.9940
epoch 2/2 *** discriminator loss: 1.0792 *** generator loss: 1.0819
epoch 2/2 *** discriminator loss: 0.9334 *** generator loss: 1.4814
epoch 2/2 *** discriminator loss: 1.1471 *** generator loss: 2.7898
epoch 2/2 *** discriminator loss: 0.9438 *** generator loss: 1.5140
epoch 2/2 *** discriminator loss: 0.9054 *** generator loss: 1.3943
epoch 2/2 *** discriminator loss: 0.8569 *** generator loss: 1.1190
epoch 2/2 *** discriminator loss: 1.1006 *** generator loss: 2.0112
epoch 2/2 *** discriminator loss: 0.8671 *** generator loss: 1.1217
epoch 2/2 *** discriminator loss: 0.9192 *** generator loss: 0.9974
epoch 2/2 *** discriminator loss: 0.8708 *** generator loss: 1.2172
epoch 2/2 *** discriminator loss: 0.9062 *** generator loss: 1.9669
epoch 2/2 *** discriminator loss: 1.2322 *** generator loss: 0.8611
epoch 2/2 *** discriminator loss: 1.0941 *** generator loss: 1.0093
epoch 2/2 *** discriminator loss: 0.9610 *** generator loss: 0.9221
epoch 2/2 *** discriminator loss: 0.8114 *** generator loss: 1.5170
epoch 2/2 *** discriminator loss: 1.0537 *** generator loss: 0.9402
epoch 2/2 *** discriminator loss: 0.9852 *** generator loss: 1.0762
epoch 2/2 *** discriminator loss: 0.9355 *** generator loss: 1.1975
epoch 2/2 *** discriminator loss: 0.9443 *** generator loss: 1.2478
epoch 2/2 *** discriminator loss: 0.9590 *** generator loss: 1.5789
epoch 2/2 *** discriminator loss: 0.9597 *** generator loss: 2.5533
epoch 2/2 *** discriminator loss: 1.0231 *** generator loss: 1.2026
epoch 2/2 *** discriminator loss: 1.0702 *** generator loss: 1.5175
epoch 2/2 *** discriminator loss: 1.0087 *** generator loss: 0.7762
epoch 2/2 *** discriminator loss: 1.0803 *** generator loss: 0.9099
epoch 2/2 *** discriminator loss: 1.0538 *** generator loss: 1.6923
epoch 2/2 *** discriminator loss: 0.9284 *** generator loss: 1.2109
epoch 2/2 *** discriminator loss: 0.9210 *** generator loss: 1.2666
epoch 2/2 *** discriminator loss: 0.9957 *** generator loss: 1.3139
epoch 2/2 *** discriminator loss: 0.9084 *** generator loss: 1.6865
epoch 2/2 *** discriminator loss: 0.7752 *** generator loss: 1.3708
epoch 2/2 *** discriminator loss: 0.9927 *** generator loss: 1.2693
epoch 2/2 *** discriminator loss: 0.8106 *** generator loss: 1.1430
epoch 2/2 *** discriminator loss: 0.9759 *** generator loss: 1.1036
epoch 2/2 *** discriminator loss: 0.9625 *** generator loss: 1.6450
epoch 2/2 *** discriminator loss: 0.9974 *** generator loss: 1.3078
epoch 2/2 *** discriminator loss: 0.9895 *** generator loss: 1.2936
epoch 2/2 *** discriminator loss: 0.8641 *** generator loss: 1.1420
epoch 2/2 *** discriminator loss: 1.0049 *** generator loss: 1.0302
epoch 2/2 *** discriminator loss: 1.0646 *** generator loss: 1.0212
epoch 2/2 *** discriminator loss: 0.9095 *** generator loss: 1.2090
epoch 2/2 *** discriminator loss: 0.9048 *** generator loss: 1.5980
epoch 2/2 *** discriminator loss: 1.0787 *** generator loss: 1.6778
epoch 2/2 *** discriminator loss: 0.9329 *** generator loss: 1.5428
epoch 2/2 *** discriminator loss: 0.9512 *** generator loss: 1.5513
epoch 2/2 *** discriminator loss: 1.1239 *** generator loss: 1.1465
epoch 2/2 *** discriminator loss: 0.9183 *** generator loss: 1.2877
epoch 2/2 *** discriminator loss: 0.9869 *** generator loss: 1.7511
epoch 2/2 *** discriminator loss: 0.9137 *** generator loss: 1.3777
epoch 2/2 *** discriminator loss: 0.9175 *** generator loss: 1.2649
epoch 2/2 *** discriminator loss: 0.9470 *** generator loss: 1.4288
epoch 2/2 *** discriminator loss: 1.0530 *** generator loss: 0.8490
epoch 2/2 *** discriminator loss: 0.9530 *** generator loss: 1.7797
epoch 2/2 *** discriminator loss: 1.0364 *** generator loss: 1.0176
epoch 2/2 *** discriminator loss: 0.8992 *** generator loss: 1.1299
epoch 2/2 *** discriminator loss: 0.8961 *** generator loss: 1.8287
epoch 2/2 *** discriminator loss: 0.9292 *** generator loss: 1.4008
epoch 2/2 *** discriminator loss: 0.9058 *** generator loss: 1.5930
epoch 2/2 *** discriminator loss: 0.9933 *** generator loss: 1.1594
epoch 2/2 *** discriminator loss: 0.9113 *** generator loss: 1.1716
epoch 2/2 *** discriminator loss: 0.9747 *** generator loss: 1.0546
epoch 2/2 *** discriminator loss: 0.8148 *** generator loss: 1.3758
epoch 2/2 *** discriminator loss: 1.0798 *** generator loss: 1.0090
epoch 2/2 *** discriminator loss: 1.4590 *** generator loss: 2.2666
epoch 2/2 *** discriminator loss: 1.0512 *** generator loss: 0.9519
epoch 2/2 *** discriminator loss: 0.9488 *** generator loss: 1.7897
epoch 2/2 *** discriminator loss: 1.1273 *** generator loss: 1.4108
epoch 2/2 *** discriminator loss: 0.9134 *** generator loss: 1.2302
epoch 2/2 *** discriminator loss: 0.9153 *** generator loss: 2.3106
epoch 2/2 *** discriminator loss: 0.9752 *** generator loss: 1.0880
epoch 2/2 *** discriminator loss: 0.9729 *** generator loss: 2.3751
epoch 2/2 *** discriminator loss: 1.0698 *** generator loss: 1.3313
epoch 2/2 *** discriminator loss: 0.9720 *** generator loss: 1.5077
epoch 2/2 *** discriminator loss: 0.9568 *** generator loss: 1.7393
epoch 2/2 *** discriminator loss: 0.9752 *** generator loss: 1.1158
epoch 2/2 *** discriminator loss: 0.8633 *** generator loss: 1.3141
epoch 2/2 *** discriminator loss: 1.0286 *** generator loss: 0.9265
epoch 2/2 *** discriminator loss: 0.8462 *** generator loss: 1.3347
epoch 2/2 *** discriminator loss: 0.9056 *** generator loss: 1.3203
epoch 2/2 *** discriminator loss: 0.8822 *** generator loss: 1.1863
epoch 2/2 *** discriminator loss: 0.8724 *** generator loss: 1.1257
epoch 2/2 *** discriminator loss: 0.9390 *** generator loss: 1.4576
epoch 2/2 *** discriminator loss: 0.9530 *** generator loss: 1.2118
epoch 2/2 *** discriminator loss: 0.8029 *** generator loss: 1.9846
epoch 2/2 *** discriminator loss: 1.0113 *** generator loss: 0.9969
epoch 2/2 *** discriminator loss: 0.8031 *** generator loss: 1.3722
epoch 2/2 *** discriminator loss: 0.7998 *** generator loss: 1.2705
epoch 2/2 *** discriminator loss: 0.9795 *** generator loss: 1.1769
epoch 2/2 *** discriminator loss: 0.9990 *** generator loss: 1.4609
epoch 2/2 *** discriminator loss: 0.8069 *** generator loss: 1.7422
epoch 2/2 *** discriminator loss: 0.9343 *** generator loss: 1.5278
epoch 2/2 *** discriminator loss: 0.9883 *** generator loss: 1.3229
epoch 2/2 *** discriminator loss: 1.0542 *** generator loss: 0.7932
epoch 2/2 *** discriminator loss: 1.2017 *** generator loss: 0.8218
epoch 2/2 *** discriminator loss: 1.0134 *** generator loss: 1.2050
epoch 2/2 *** discriminator loss: 1.0822 *** generator loss: 1.0912
epoch 2/2 *** discriminator loss: 1.0685 *** generator loss: 1.2147
epoch 2/2 *** discriminator loss: 0.9687 *** generator loss: 1.5770
epoch 2/2 *** discriminator loss: 0.8361 *** generator loss: 1.4030
epoch 2/2 *** discriminator loss: 0.9561 *** generator loss: 1.1939
epoch 2/2 *** discriminator loss: 1.0502 *** generator loss: 0.9821
epoch 2/2 *** discriminator loss: 1.0327 *** generator loss: 1.1295
epoch 2/2 *** discriminator loss: 1.0924 *** generator loss: 0.7424
epoch 2/2 *** discriminator loss: 0.8666 *** generator loss: 1.2519
epoch 2/2 *** discriminator loss: 0.8761 *** generator loss: 1.6682
epoch 2/2 *** discriminator loss: 0.9614 *** generator loss: 1.1425
epoch 2/2 *** discriminator loss: 1.0733 *** generator loss: 1.0919
epoch 2/2 *** discriminator loss: 0.7531 *** generator loss: 1.3409
epoch 2/2 *** discriminator loss: 1.0944 *** generator loss: 1.4312
epoch 2/2 *** discriminator loss: 0.9450 *** generator loss: 1.2963
epoch 2/2 *** discriminator loss: 1.0222 *** generator loss: 1.5175
epoch 2/2 *** discriminator loss: 1.0405 *** generator loss: 1.3377
epoch 2/2 *** discriminator loss: 0.7545 *** generator loss: 1.6073
epoch 2/2 *** discriminator loss: 0.9931 *** generator loss: 1.2998
epoch 2/2 *** discriminator loss: 1.1579 *** generator loss: 1.8873
epoch 2/2 *** discriminator loss: 0.9213 *** generator loss: 1.3664
epoch 2/2 *** discriminator loss: 0.9868 *** generator loss: 1.7045
epoch 2/2 *** discriminator loss: 1.1738 *** generator loss: 0.9932
epoch 2/2 *** discriminator loss: 0.8176 *** generator loss: 1.1604
epoch 2/2 *** discriminator loss: 0.9744 *** generator loss: 1.2993
epoch 2/2 *** discriminator loss: 0.9187 *** generator loss: 1.5689
epoch 2/2 *** discriminator loss: 1.0116 *** generator loss: 1.5888
epoch 2/2 *** discriminator loss: 0.8654 *** generator loss: 1.1822
epoch 2/2 *** discriminator loss: 1.0532 *** generator loss: 0.8851
epoch 2/2 *** discriminator loss: 0.9663 *** generator loss: 1.8747
epoch 2/2 *** discriminator loss: 1.1356 *** generator loss: 0.8258
epoch 2/2 *** discriminator loss: 0.8988 *** generator loss: 1.2355
epoch 2/2 *** discriminator loss: 0.9384 *** generator loss: 1.6476
epoch 2/2 *** discriminator loss: 1.0910 *** generator loss: 0.8627
epoch 2/2 *** discriminator loss: 1.0262 *** generator loss: 0.9492
epoch 2/2 *** discriminator loss: 0.9877 *** generator loss: 1.2180
epoch 2/2 *** discriminator loss: 0.9682 *** generator loss: 1.3248
epoch 2/2 *** discriminator loss: 1.0435 *** generator loss: 1.7018
epoch 2/2 *** discriminator loss: 0.9510 *** generator loss: 0.8173
epoch 2/2 *** discriminator loss: 0.8988 *** generator loss: 1.6119
epoch 2/2 *** discriminator loss: 0.9326 *** generator loss: 1.2577
epoch 2/2 *** discriminator loss: 0.9068 *** generator loss: 1.3193
epoch 2/2 *** discriminator loss: 0.7994 *** generator loss: 1.1933
epoch 2/2 *** discriminator loss: 1.0595 *** generator loss: 0.8805
epoch 2/2 *** discriminator loss: 1.0020 *** generator loss: 1.1933
epoch 2/2 *** discriminator loss: 0.8879 *** generator loss: 1.3516
epoch 2/2 *** discriminator loss: 0.9722 *** generator loss: 0.9847
epoch 2/2 *** discriminator loss: 0.9866 *** generator loss: 1.0643
epoch 2/2 *** discriminator loss: 0.9674 *** generator loss: 1.3444
epoch 2/2 *** discriminator loss: 1.0401 *** generator loss: 1.0186
epoch 2/2 *** discriminator loss: 0.9585 *** generator loss: 1.9166
epoch 2/2 *** discriminator loss: 0.9069 *** generator loss: 1.5076
epoch 2/2 *** discriminator loss: 1.1422 *** generator loss: 0.7640
epoch 2/2 *** discriminator loss: 1.0824 *** generator loss: 0.7316
epoch 2/2 *** discriminator loss: 1.4796 *** generator loss: 2.4066
epoch 2/2 *** discriminator loss: 1.0167 *** generator loss: 1.0732
epoch 2/2 *** discriminator loss: 1.0133 *** generator loss: 0.9232
epoch 2/2 *** discriminator loss: 0.7617 *** generator loss: 1.7278
epoch 2/2 *** discriminator loss: 1.1408 *** generator loss: 1.0102
epoch 2/2 *** discriminator loss: 0.8869 *** generator loss: 1.2572
epoch 2/2 *** discriminator loss: 1.0113 *** generator loss: 1.0753
epoch 2/2 *** discriminator loss: 1.0880 *** generator loss: 2.5654
epoch 2/2 *** discriminator loss: 0.8713 *** generator loss: 1.2226
epoch 2/2 *** discriminator loss: 0.9816 *** generator loss: 0.9696
epoch 2/2 *** discriminator loss: 0.9638 *** generator loss: 1.0980
epoch 2/2 *** discriminator loss: 0.9683 *** generator loss: 1.2922
epoch 2/2 *** discriminator loss: 0.9350 *** generator loss: 1.5224
epoch 2/2 *** discriminator loss: 0.9258 *** generator loss: 1.2490
epoch 2/2 *** discriminator loss: 1.0579 *** generator loss: 0.9795
epoch 2/2 *** discriminator loss: 1.0835 *** generator loss: 0.9734
epoch 2/2 *** discriminator loss: 0.8590 *** generator loss: 1.5036
epoch 2/2 *** discriminator loss: 1.2437 *** generator loss: 0.7599
epoch 2/2 *** discriminator loss: 0.8973 *** generator loss: 1.0937
epoch 2/2 *** discriminator loss: 1.1901 *** generator loss: 0.7098
epoch 2/2 *** discriminator loss: 1.0052 *** generator loss: 1.4915
epoch 2/2 *** discriminator loss: 0.8051 *** generator loss: 1.8986
epoch 2/2 *** discriminator loss: 0.8507 *** generator loss: 1.4427
epoch 2/2 *** discriminator loss: 1.1346 *** generator loss: 1.6921
epoch 2/2 *** discriminator loss: 0.8479 *** generator loss: 1.9376
epoch 2/2 *** discriminator loss: 0.9039 *** generator loss: 1.1695
epoch 2/2 *** discriminator loss: 0.9753 *** generator loss: 1.1722
epoch 2/2 *** discriminator loss: 1.2047 *** generator loss: 0.7553
epoch 2/2 *** discriminator loss: 0.9858 *** generator loss: 1.0675
epoch 2/2 *** discriminator loss: 1.0480 *** generator loss: 1.2174
epoch 2/2 *** discriminator loss: 0.9678 *** generator loss: 1.6566
epoch 2/2 *** discriminator loss: 0.9239 *** generator loss: 1.1864
epoch 2/2 *** discriminator loss: 1.0522 *** generator loss: 1.0398
epoch 2/2 *** discriminator loss: 1.0142 *** generator loss: 1.0733
epoch 2/2 *** discriminator loss: 1.0371 *** generator loss: 1.0425
epoch 2/2 *** discriminator loss: 0.8936 *** generator loss: 1.3573
epoch 2/2 *** discriminator loss: 0.9970 *** generator loss: 1.1596
epoch 2/2 *** discriminator loss: 1.0923 *** generator loss: 1.8749
epoch 2/2 *** discriminator loss: 0.8762 *** generator loss: 1.2334
epoch 2/2 *** discriminator loss: 0.9548 *** generator loss: 1.3656
epoch 2/2 *** discriminator loss: 0.8432 *** generator loss: 1.8154
epoch 2/2 *** discriminator loss: 0.9666 *** generator loss: 1.0095
epoch 2/2 *** discriminator loss: 0.8288 *** generator loss: 1.4501
epoch 2/2 *** discriminator loss: 1.0888 *** generator loss: 1.5073
epoch 2/2 *** discriminator loss: 1.0188 *** generator loss: 1.5022
epoch 2/2 *** discriminator loss: 0.9856 *** generator loss: 1.2931
epoch 2/2 *** discriminator loss: 0.8856 *** generator loss: 1.3623
epoch 2/2 *** discriminator loss: 0.8257 *** generator loss: 1.2282
epoch 2/2 *** discriminator loss: 1.1435 *** generator loss: 0.7964
epoch 2/2 *** discriminator loss: 0.8773 *** generator loss: 1.3084
epoch 2/2 *** discriminator loss: 0.9596 *** generator loss: 1.1520
epoch 2/2 *** discriminator loss: 0.9682 *** generator loss: 1.4431
epoch 2/2 *** discriminator loss: 0.7819 *** generator loss: 1.3719
epoch 2/2 *** discriminator loss: 0.9966 *** generator loss: 1.3843
epoch 2/2 *** discriminator loss: 0.8256 *** generator loss: 1.5637

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [20]:
batch_size = 32
z_dim = 100
learning_rate = 0.00035
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
epoch 1/1 *** discriminator loss: 1.7971 *** generator loss: 1.0052
epoch 1/1 *** discriminator loss: 1.0955 *** generator loss: 4.1848
epoch 1/1 *** discriminator loss: 1.1377 *** generator loss: 4.4336
epoch 1/1 *** discriminator loss: 1.6614 *** generator loss: 3.9760
epoch 1/1 *** discriminator loss: 1.2191 *** generator loss: 4.1880
epoch 1/1 *** discriminator loss: 1.1724 *** generator loss: 4.7975
epoch 1/1 *** discriminator loss: 0.6932 *** generator loss: 4.5518
epoch 1/1 *** discriminator loss: 0.6494 *** generator loss: 4.0086
epoch 1/1 *** discriminator loss: 1.0918 *** generator loss: 5.0815
epoch 1/1 *** discriminator loss: 0.5462 *** generator loss: 3.5764
epoch 1/1 *** discriminator loss: 0.7292 *** generator loss: 4.0021
epoch 1/1 *** discriminator loss: 0.7214 *** generator loss: 2.6471
epoch 1/1 *** discriminator loss: 0.7080 *** generator loss: 2.3035
epoch 1/1 *** discriminator loss: 0.7259 *** generator loss: 2.5766
epoch 1/1 *** discriminator loss: 0.8705 *** generator loss: 1.9261
epoch 1/1 *** discriminator loss: 0.9197 *** generator loss: 1.3344
epoch 1/1 *** discriminator loss: 0.8363 *** generator loss: 1.8143
epoch 1/1 *** discriminator loss: 0.8072 *** generator loss: 2.1434
epoch 1/1 *** discriminator loss: 0.9101 *** generator loss: 2.1899
epoch 1/1 *** discriminator loss: 1.0476 *** generator loss: 1.1610
epoch 1/1 *** discriminator loss: 0.9925 *** generator loss: 1.5258
epoch 1/1 *** discriminator loss: 1.0341 *** generator loss: 1.3946
epoch 1/1 *** discriminator loss: 0.9068 *** generator loss: 2.1330
epoch 1/1 *** discriminator loss: 1.0223 *** generator loss: 1.7519
epoch 1/1 *** discriminator loss: 1.2922 *** generator loss: 2.0859
epoch 1/1 *** discriminator loss: 1.0120 *** generator loss: 1.3450
epoch 1/1 *** discriminator loss: 1.0117 *** generator loss: 1.5602
epoch 1/1 *** discriminator loss: 1.2088 *** generator loss: 1.5815
epoch 1/1 *** discriminator loss: 1.1465 *** generator loss: 1.6369
epoch 1/1 *** discriminator loss: 0.9374 *** generator loss: 2.0622
epoch 1/1 *** discriminator loss: 1.0837 *** generator loss: 1.5676
epoch 1/1 *** discriminator loss: 1.4153 *** generator loss: 1.2589
epoch 1/1 *** discriminator loss: 1.3994 *** generator loss: 1.2295
epoch 1/1 *** discriminator loss: 1.4080 *** generator loss: 1.5381
epoch 1/1 *** discriminator loss: 1.4565 *** generator loss: 1.4064
epoch 1/1 *** discriminator loss: 1.6054 *** generator loss: 0.9595
epoch 1/1 *** discriminator loss: 1.1385 *** generator loss: 1.1915
epoch 1/1 *** discriminator loss: 1.2170 *** generator loss: 1.4042
epoch 1/1 *** discriminator loss: 1.2221 *** generator loss: 0.9370
epoch 1/1 *** discriminator loss: 1.3400 *** generator loss: 0.8680
epoch 1/1 *** discriminator loss: 1.4174 *** generator loss: 1.3776
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epoch 1/1 *** discriminator loss: 1.2998 *** generator loss: 0.9726
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epoch 1/1 *** discriminator loss: 1.1780 *** generator loss: 1.0187
epoch 1/1 *** discriminator loss: 1.2873 *** generator loss: 1.0168
epoch 1/1 *** discriminator loss: 1.3651 *** generator loss: 0.8124

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.